The AI work that holds up in production is rarely the demo that impresses in a meeting. It is narrow, measurable and wrapped in a workflow someone already owns.
Reliable categories
Document extraction: turning invoices, forms and scanned records into structured fields a system can query.
Classification and triage: routing incoming requests to the right queue, with a confidence threshold that escalates anything uncertain to a person.
Drafting: producing a first version of a reply, summary or report that a human edits and approves before it leaves the building.
Design for the wrong answer
A model will be wrong sometimes. The system around it decides whether that is a minor correction or an incident.
Practical safeguards: keep a human approval step wherever the output has consequences, log every input and output so a decision can be reviewed, and give the interface an obvious path to override.
Measure against the manual baseline
Before integrating anything, record how long the task takes today and how often it goes wrong. Without that baseline there is no way to tell whether the feature earned its place.
If the automated path is not clearly better than the manual one on both time and accuracy, the honest conclusion is to leave the process alone.
Takeaway
Choose one task with a measurable baseline, keep a human in the loop where it matters, and expand only after the numbers hold.